To guarantee safe motion in cluttered environment in presence of uncertainties, various efficient robust motion-planning algorithms have been proposed in literature. The strategies proposed in [1]}, [2]}, [3]}, [4]}, [5]}, [6]} deal with the motion-planning problem for linear systems affected by uncertainties. The results in [1]}, [2]} propose robust RRT strategies, which extend the standard RRT algorithm to the cases of linear systems having additive noise and uncertainty in state estimation. The approaches in [3]}, [5]} guarantee safe motion for systems with linear models, in environments with unknown obstacles. An efficient invariant-set based robust motion planner is proposed in [6]}, which handles linear systems with parametric uncertainty as well as bounded additive disturbances. Although these approaches are efficient robust motion-planning solutions, they are not suitable to handle systems with nonlinear models, which is mostly the case in practise. The approaches in [12]}, [13]}, [14]}, [15]}, [16]} address the robust motion-planning problem for nonlinear systems with uncertainties. A learning-based robust motion planner is proposed in [12]}, which utilizes contraction theory to generate a safety certificate for trajectories of a nonlinear system affected by additive disturbances. However, to guarantee performance, the strategy in [12]} requires complete knowledge of the obstacles in the environment, which is restrictive for practical implementations. A control barrier function based robust RRT strategy is proposed for uncertain nonlinear systems in [13]} and the performance is validated through numerical simulations. The strategies in [14]}, [15]} generate funnel libraries to handle uncertainties in robust motion-planning. These strategies can efficiently handle nonlinear systems under the effect of uncertainties, such as external disturbances and obstacles which appear during runtime. However, as the funnels vary in shape and size, additional efforts/conditions are required to maintain feasible composition of the funnels during online implementation. The drawback associated with the funnel library based motion-planning is circumvented in the strategy in [16]}, which generates a fixed-size tube along a planned nominal trajectory, to capture the motion of the nonlinear system under uncertainty. However, the approach in [16]} depends upon an external motion planner to compute the nominal trajectory and it requires replanning during runtime if the currently planned path becomes infeasible due to the appearance of any previously unforeseen obstacle.
